Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations5673
Missing cells349
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory893.5 B

Variable types

Text3
Categorical17
Numeric6

Alerts

cant_apercibimientos has constant value "0.0" Constant
cant_representante has constant value "0.0" Constant
cant_MontoLimite has constant value "0.0" Constant
cluster_k5 has constant value "4" Constant
Estado is highly overall correlated with periodo_preinscripcionHigh correlation
TipoSocietario is highly overall correlated with periodo_preinscripcionHigh correlation
anio_preinscripcion is highly overall correlated with antiguedad and 4 other fieldsHigh correlation
antiguedad is highly overall correlated with anio_preinscripcion and 4 other fieldsHigh correlation
cant_Apoderado is highly overall correlated with anio_preinscripcion and 6 other fieldsHigh correlation
cant_antecedentes is highly overall correlated with cant_suspensionesHigh correlation
cant_autenticado is highly overall correlated with anio_preinscripcion and 5 other fieldsHigh correlation
cant_noAutenticado is highly overall correlated with cant_Apoderado and 1 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with monto_total_adjudicadoHigh correlation
cant_sinMontoLimite is highly overall correlated with anio_preinscripcion and 6 other fieldsHigh correlation
cant_suspensiones is highly overall correlated with cant_antecedentesHigh correlation
dtotal_articulos_provee is highly overall correlated with periodo_preinscripcionHigh correlation
monto_total_adjudicado is highly overall correlated with cant_procesos_adjudicadoHigh correlation
periodo_preinscripcion is highly overall correlated with Estado and 8 other fieldsHigh correlation
provincia is highly overall correlated with cant_Apoderado and 3 other fieldsHigh correlation
Estado is highly imbalanced (62.2%) Imbalance
TipoSocietario is highly imbalanced (51.0%) Imbalance
cant_suspensiones is highly imbalanced (98.5%) Imbalance
cant_antecedentes is highly imbalanced (98.5%) Imbalance
cant_Apoderado is highly imbalanced (62.4%) Imbalance
cant_autenticado is highly imbalanced (69.9%) Imbalance
cant_noAutenticado is highly imbalanced (78.2%) Imbalance
cant_sinMontoLimite is highly imbalanced (62.4%) Imbalance
Estado has 99 (1.7%) missing values Missing
TipoSocietario has 99 (1.7%) missing values Missing
dtotal_articulos_provee has 99 (1.7%) missing values Missing
CUIT has unique values Unique
periodo_preinscripcion has 99 (1.7%) zeros Zeros
monto_total_adjudicado has 71 (1.3%) zeros Zeros
antiguedad has 802 (14.1%) zeros Zeros
cant_socios has 3773 (66.5%) zeros Zeros
total_articulos_provee has 99 (1.7%) zeros Zeros

Reproduction

Analysis started2025-07-08 14:19:47.985236
Analysis finished2025-07-08 14:19:53.233446
Duration5.25 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct5673
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.9 KiB
2025-07-08T11:19:53.371691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length11
Mean length10.965627
Min length3

Characters and Unicode

Total characters62208
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5673 ?
Unique (%)100.0%

Sample

1st row27236909900
2nd row30569211685
3rd row30708415487
4th row30708538317
5th row30521417311
ValueCountFrequency (%)
30707787070 1
 
< 0.1%
27331126530 1
 
< 0.1%
27236909900 1
 
< 0.1%
30569211685 1
 
< 0.1%
30708415487 1
 
< 0.1%
30708538317 1
 
< 0.1%
30521417311 1
 
< 0.1%
20305924076 1
 
< 0.1%
20082883240 1
 
< 0.1%
27281848467 1
 
< 0.1%
Other values (5663) 5663
99.8%
2025-07-08T11:19:53.608359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 9533
15.3%
0 9349
15.0%
3 7424
11.9%
7 6492
10.4%
1 6279
10.1%
6 4754
7.6%
9 4690
7.5%
4 4631
7.4%
5 4560
7.3%
8 4418
7.1%
Other values (20) 78
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62208
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 9533
15.3%
0 9349
15.0%
3 7424
11.9%
7 6492
10.4%
1 6279
10.1%
6 4754
7.6%
9 4690
7.5%
4 4631
7.4%
5 4560
7.3%
8 4418
7.1%
Other values (20) 78
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62208
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 9533
15.3%
0 9349
15.0%
3 7424
11.9%
7 6492
10.4%
1 6279
10.1%
6 4754
7.6%
9 4690
7.5%
4 4631
7.4%
5 4560
7.3%
8 4418
7.1%
Other values (20) 78
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62208
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 9533
15.3%
0 9349
15.0%
3 7424
11.9%
7 6492
10.4%
1 6279
10.1%
6 4754
7.6%
9 4690
7.5%
4 4631
7.4%
5 4560
7.3%
8 4418
7.1%
Other values (20) 78
 
0.1%

Nombre
Text

Distinct4649
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Memory size470.3 KiB
2025-07-08T11:19:53.766595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length102
Median length71
Mean length17.249251
Min length1

Characters and Unicode

Total characters97855
Distinct characters98
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4645 ?
Unique (%)81.9%

Sample

1st rowEMR VENTAS & SERVICIOS
2nd rowElectricidad Chiclana de R. Santoianni y O.S. Rodriguez
3rd rowYLUM S.A.
4th rowCAROLS SA
5th rowCONFECCIONES JOSE CONTARTESE Y CIA S.R.L.
ValueCountFrequency (%)
sin 1023
 
6.8%
datos 1022
 
6.8%
s.a 591
 
3.9%
srl 431
 
2.8%
de 324
 
2.1%
sa 303
 
2.0%
s.r.l 214
 
1.4%
y 189
 
1.2%
servicios 153
 
1.0%
argentina 118
 
0.8%
Other values (5709) 10769
71.1%
2025-07-08T11:19:54.052456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9465
 
9.7%
A 7058
 
7.2%
S 4796
 
4.9%
E 4648
 
4.7%
I 4487
 
4.6%
R 4395
 
4.5%
a 4098
 
4.2%
O 3765
 
3.8%
s 3665
 
3.7%
i 3612
 
3.7%
Other values (88) 47866
48.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9465
 
9.7%
A 7058
 
7.2%
S 4796
 
4.9%
E 4648
 
4.7%
I 4487
 
4.6%
R 4395
 
4.5%
a 4098
 
4.2%
O 3765
 
3.8%
s 3665
 
3.7%
i 3612
 
3.7%
Other values (88) 47866
48.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9465
 
9.7%
A 7058
 
7.2%
S 4796
 
4.9%
E 4648
 
4.7%
I 4487
 
4.6%
R 4395
 
4.5%
a 4098
 
4.2%
O 3765
 
3.8%
s 3665
 
3.7%
i 3612
 
3.7%
Other values (88) 47866
48.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9465
 
9.7%
A 7058
 
7.2%
S 4796
 
4.9%
E 4648
 
4.7%
I 4487
 
4.6%
R 4395
 
4.5%
a 4098
 
4.2%
O 3765
 
3.8%
s 3665
 
3.7%
i 3612
 
3.7%
Other values (88) 47866
48.9%
Distinct1614
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Memory size415.4 KiB
2025-07-08T11:19:54.199071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9825489
Min length9

Characters and Unicode

Total characters56631
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique514 ?
Unique (%)9.1%

Sample

1st row04/10/2016
2nd row18/08/2016
3rd row24/08/2016
4th row09/09/2016
5th row12/09/2016
ValueCountFrequency (%)
datos 99
 
1.7%
sin 99
 
1.7%
17/11/2021 27
 
0.5%
03/11/2016 21
 
0.4%
17/11/2016 20
 
0.3%
06/12/2016 19
 
0.3%
23/11/2016 17
 
0.3%
21/06/2017 16
 
0.3%
16/11/2016 16
 
0.3%
01/02/2017 16
 
0.3%
Other values (1605) 5422
93.9%
2025-07-08T11:19:54.452543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12777
22.6%
/ 11148
19.7%
2 10323
18.2%
1 9700
17.1%
7 2922
 
5.2%
8 2063
 
3.6%
6 1806
 
3.2%
9 1629
 
2.9%
3 1329
 
2.3%
5 1061
 
1.9%
Other values (9) 1873
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56631
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12777
22.6%
/ 11148
19.7%
2 10323
18.2%
1 9700
17.1%
7 2922
 
5.2%
8 2063
 
3.6%
6 1806
 
3.2%
9 1629
 
2.9%
3 1329
 
2.3%
5 1061
 
1.9%
Other values (9) 1873
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56631
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12777
22.6%
/ 11148
19.7%
2 10323
18.2%
1 9700
17.1%
7 2922
 
5.2%
8 2063
 
3.6%
6 1806
 
3.2%
9 1629
 
2.9%
3 1329
 
2.3%
5 1061
 
1.9%
Other values (9) 1873
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56631
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12777
22.6%
/ 11148
19.7%
2 10323
18.2%
1 9700
17.1%
7 2922
 
5.2%
8 2063
 
3.6%
6 1806
 
3.2%
9 1629
 
2.9%
3 1329
 
2.3%
5 1061
 
1.9%
Other values (9) 1873
 
3.3%

Estado
Categorical

High correlation  Imbalance  Missing 

Distinct9
Distinct (%)0.2%
Missing99
Missing (%)1.7%
Memory size429.3 KiB
Inscripto
4312 
Pre Inscripto
543 
Desactualizado Por Documentos Vencidos
471 
Desactualizado Por Mantencion Formulario
 
107
Con Solicitud De Baja
 
59
Other values (4)
 
82

Length

Max length40
Median length9
Mean length12.734661
Min length9

Characters and Unicode

Total characters70983
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowInscripto
2nd rowDesactualizado Por Documentos Vencidos
3rd rowInscripto
4th rowInscripto
5th rowInscripto

Common Values

ValueCountFrequency (%)
Inscripto 4312
76.0%
Pre Inscripto 543
 
9.6%
Desactualizado Por Documentos Vencidos 471
 
8.3%
Desactualizado Por Mantencion Formulario 107
 
1.9%
Con Solicitud De Baja 59
 
1.0%
Desactualizado Por Clase 58
 
1.0%
En Evaluacion 22
 
0.4%
Suspendido 1
 
< 0.1%
Dar De Baja 1
 
< 0.1%
(Missing) 99
 
1.7%

Length

2025-07-08T11:19:54.564194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:54.667772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 4855
59.4%
desactualizado 636
 
7.8%
por 636
 
7.8%
pre 543
 
6.6%
documentos 471
 
5.8%
vencidos 471
 
5.8%
mantencion 107
 
1.3%
formulario 107
 
1.3%
de 60
 
0.7%
baja 60
 
0.7%
Other values (7) 222
 
2.7%

Most occurring characters

ValueCountFrequency (%)
o 8002
11.3%
c 6621
9.3%
s 6492
9.1%
i 6317
8.9%
r 6249
8.8%
n 6222
8.8%
t 6128
8.6%
p 4856
 
6.8%
I 4855
 
6.8%
2594
 
3.7%
Other values (18) 12647
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 8002
11.3%
c 6621
9.3%
s 6492
9.1%
i 6317
8.9%
r 6249
8.8%
n 6222
8.8%
t 6128
8.6%
p 4856
 
6.8%
I 4855
 
6.8%
2594
 
3.7%
Other values (18) 12647
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 8002
11.3%
c 6621
9.3%
s 6492
9.1%
i 6317
8.9%
r 6249
8.8%
n 6222
8.8%
t 6128
8.6%
p 4856
 
6.8%
I 4855
 
6.8%
2594
 
3.7%
Other values (18) 12647
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 8002
11.3%
c 6621
9.3%
s 6492
9.1%
i 6317
8.9%
r 6249
8.8%
n 6222
8.8%
t 6128
8.6%
p 4856
 
6.8%
I 4855
 
6.8%
2594
 
3.7%
Other values (18) 12647
17.8%

TipoSocietario
Categorical

High correlation  Imbalance  Missing 

Distinct10
Distinct (%)0.2%
Missing99
Missing (%)1.7%
Memory size640.7 KiB
Persona Física
3579 
Sociedad Anónima
968 
Sociedad Responsabilidad Limitada
672 
Otras Formas Societarias
 
101
Persona Jurídica Extranjero Sin Sucursal
 
79
Other values (5)
 
175

Length

Max length40
Median length14
Mean length17.360065
Min length12

Characters and Unicode

Total characters96765
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPersona Física
2nd rowSociedades De Hecho
3rd rowSociedad Anónima
4th rowSociedad Anónima
5th rowSociedad Responsabilidad Limitada

Common Values

ValueCountFrequency (%)
Persona Física 3579
63.1%
Sociedad Anónima 968
 
17.1%
Sociedad Responsabilidad Limitada 672
 
11.8%
Otras Formas Societarias 101
 
1.8%
Persona Jurídica Extranjero Sin Sucursal 79
 
1.4%
Organismo Publico 71
 
1.3%
Sociedades De Hecho 45
 
0.8%
Cooperativas 34
 
0.6%
Persona Física Extranjero No Residente 24
 
0.4%
Unión Transitoria de Empresas 1
 
< 0.1%
(Missing) 99
 
1.7%

Length

2025-07-08T11:19:54.805917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:54.896719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
persona 3682
30.1%
física 3603
29.4%
sociedad 1640
13.4%
anónima 968
 
7.9%
responsabilidad 672
 
5.5%
limitada 672
 
5.5%
extranjero 103
 
0.8%
otras 101
 
0.8%
formas 101
 
0.8%
societarias 101
 
0.8%
Other values (14) 600
 
4.9%

Most occurring characters

ValueCountFrequency (%)
a 13433
13.9%
i 9507
9.8%
s 9188
9.5%
6669
 
6.9%
o 6624
 
6.8%
n 6570
 
6.8%
e 6486
 
6.7%
c 5663
 
5.9%
d 5490
 
5.7%
r 4457
 
4.6%
Other values (28) 22678
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 13433
13.9%
i 9507
9.8%
s 9188
9.5%
6669
 
6.9%
o 6624
 
6.8%
n 6570
 
6.8%
e 6486
 
6.7%
c 5663
 
5.9%
d 5490
 
5.7%
r 4457
 
4.6%
Other values (28) 22678
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 13433
13.9%
i 9507
9.8%
s 9188
9.5%
6669
 
6.9%
o 6624
 
6.8%
n 6570
 
6.8%
e 6486
 
6.7%
c 5663
 
5.9%
d 5490
 
5.7%
r 4457
 
4.6%
Other values (28) 22678
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 13433
13.9%
i 9507
9.8%
s 9188
9.5%
6669
 
6.9%
o 6624
 
6.8%
n 6570
 
6.8%
e 6486
 
6.7%
c 5663
 
5.9%
d 5490
 
5.7%
r 4457
 
4.6%
Other values (28) 22678
23.4%

periodo_preinscripcion
Real number (ℝ)

High correlation  Zeros 

Distinct80
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198302.86
Minimum0
Maximum202303
Zeros99
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size88.6 KiB
2025-07-08T11:19:55.488907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile201610
Q1201704
median201802
Q3201909
95-th percentile202201
Maximum202303
Range202303
Interquartile range (IQR)205

Descriptive statistics

Standard deviation26430.798
Coefficient of variation (CV)0.13328501
Kurtosis52.363457
Mean198302.86
Median Absolute Deviation (MAD)100
Skewness-7.3717434
Sum1.1249721 × 109
Variance6.9858708 × 108
MonotonicityNot monotonic
2025-07-08T11:19:55.628940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201611 263
 
4.6%
201706 205
 
3.6%
201701 204
 
3.6%
201705 194
 
3.4%
201612 186
 
3.3%
201703 182
 
3.2%
201704 175
 
3.1%
201702 167
 
2.9%
201708 166
 
2.9%
201707 152
 
2.7%
Other values (70) 3779
66.6%
ValueCountFrequency (%)
0 99
 
1.7%
201607 16
 
0.3%
201608 66
 
1.2%
201609 54
 
1.0%
201610 116
2.0%
201611 263
4.6%
201612 186
3.3%
201701 204
3.6%
201702 167
2.9%
201703 182
3.2%
ValueCountFrequency (%)
202303 1
 
< 0.1%
202212 1
 
< 0.1%
202211 5
 
0.1%
202210 11
 
0.2%
202209 35
0.6%
202208 37
0.7%
202207 20
0.4%
202206 27
0.5%
202205 37
0.7%
202204 38
0.7%

anio_preinscripcion
Categorical

High correlation 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size382.7 KiB
2017
1886 
2018
1061 
2016
701 
2019
636 
2021
509 
Other values (4)
880 

Length

Max length9
Median length4
Mean length4.0872554
Min length4

Characters and Unicode

Total characters23187
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2017 1886
33.2%
2018 1061
18.7%
2016 701
 
12.4%
2019 636
 
11.2%
2021 509
 
9.0%
2020 488
 
8.6%
2022 292
 
5.1%
sin datos 99
 
1.7%
2023 1
 
< 0.1%

Length

2025-07-08T11:19:55.744708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:55.833385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2017 1886
32.7%
2018 1061
18.4%
2016 701
 
12.1%
2019 636
 
11.0%
2021 509
 
8.8%
2020 488
 
8.5%
2022 292
 
5.1%
sin 99
 
1.7%
datos 99
 
1.7%
2023 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 7156
30.9%
0 6062
26.1%
1 4793
20.7%
7 1886
 
8.1%
8 1061
 
4.6%
6 701
 
3.0%
9 636
 
2.7%
s 198
 
0.9%
i 99
 
0.4%
n 99
 
0.4%
Other values (6) 496
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 7156
30.9%
0 6062
26.1%
1 4793
20.7%
7 1886
 
8.1%
8 1061
 
4.6%
6 701
 
3.0%
9 636
 
2.7%
s 198
 
0.9%
i 99
 
0.4%
n 99
 
0.4%
Other values (6) 496
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 7156
30.9%
0 6062
26.1%
1 4793
20.7%
7 1886
 
8.1%
8 1061
 
4.6%
6 701
 
3.0%
9 636
 
2.7%
s 198
 
0.9%
i 99
 
0.4%
n 99
 
0.4%
Other values (6) 496
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 7156
30.9%
0 6062
26.1%
1 4793
20.7%
7 1886
 
8.1%
8 1061
 
4.6%
6 701
 
3.0%
9 636
 
2.7%
s 198
 
0.9%
i 99
 
0.4%
n 99
 
0.4%
Other values (6) 496
 
2.1%

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct142
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6918738
Minimum0
Maximum551
Zeros26
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size88.6 KiB
2025-07-08T11:19:55.968958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile30
Maximum551
Range551
Interquartile range (IQR)4

Descriptive statistics

Standard deviation25.282478
Coefficient of variation (CV)3.2869076
Kurtosis141.4012
Mean7.6918738
Median Absolute Deviation (MAD)1
Skewness10.137917
Sum43636
Variance639.2037
MonotonicityNot monotonic
2025-07-08T11:19:56.099516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2339
41.2%
2 1004
17.7%
3 522
 
9.2%
4 333
 
5.9%
5 193
 
3.4%
6 161
 
2.8%
7 122
 
2.2%
8 85
 
1.5%
9 79
 
1.4%
11 66
 
1.2%
Other values (132) 769
 
13.6%
ValueCountFrequency (%)
0 26
 
0.5%
1 2339
41.2%
2 1004
17.7%
3 522
 
9.2%
4 333
 
5.9%
5 193
 
3.4%
6 161
 
2.8%
7 122
 
2.2%
8 85
 
1.5%
9 79
 
1.4%
ValueCountFrequency (%)
551 1
< 0.1%
525 1
< 0.1%
438 1
< 0.1%
382 1
< 0.1%
361 1
< 0.1%
350 1
< 0.1%
337 1
< 0.1%
314 1
< 0.1%
311 1
< 0.1%
302 1
< 0.1%

monto_total_adjudicado
Real number (ℝ)

High correlation  Zeros 

Distinct5460
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32965348
Minimum0
Maximum4.8485423 × 109
Zeros71
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size88.6 KiB
2025-07-08T11:19:56.218762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25934.677
Q1434307.24
median2175009.9
Q310794995
95-th percentile1.053158 × 108
Maximum4.8485423 × 109
Range4.8485423 × 109
Interquartile range (IQR)10360688

Descriptive statistics

Standard deviation1.8980152 × 108
Coefficient of variation (CV)5.757607
Kurtosis263.81109
Mean32965348
Median Absolute Deviation (MAD)2075766.7
Skewness14.592756
Sum1.8701242 × 1011
Variance3.6024615 × 1016
MonotonicityNot monotonic
2025-07-08T11:19:56.340529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
1.3%
6710526.316 7
 
0.1%
255000 4
 
0.1%
85000 4
 
0.1%
510000 4
 
0.1%
40800 4
 
0.1%
240428.5714 4
 
0.1%
114750 4
 
0.1%
526451.6129 4
 
0.1%
21857.14286 4
 
0.1%
Other values (5450) 5563
98.1%
ValueCountFrequency (%)
0 71
1.3%
0.010851064 1
 
< 0.1%
0.056666667 1
 
< 0.1%
0.80952381 1
 
< 0.1%
1.65952381 1
 
< 0.1%
2.487804878 2
 
< 0.1%
12.648 1
 
< 0.1%
120.2142857 1
 
< 0.1%
141.24 1
 
< 0.1%
154.1027027 1
 
< 0.1%
ValueCountFrequency (%)
4848542284 1
< 0.1%
4091786671 1
< 0.1%
3979415666 1
< 0.1%
3673630255 1
< 0.1%
3624943302 1
< 0.1%
3427083522 1
< 0.1%
3144931766 1
< 0.1%
2788980416 1
< 0.1%
2747532303 1
< 0.1%
2525171710 1
< 0.1%

antiguedad
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.801516
Minimum-1
Maximum5
Zeros802
Zeros (%)14.1%
Negative99
Negative (%)1.7%
Memory size88.6 KiB
2025-07-08T11:19:56.416404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6719976
Coefficient of variation (CV)0.59681888
Kurtosis-0.83928126
Mean2.801516
Median Absolute Deviation (MAD)1
Skewness-0.58672709
Sum15893
Variance2.795576
MonotonicityNot monotonic
2025-07-08T11:19:56.501796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 1886
33.2%
3 1061
18.7%
0 802
14.1%
5 701
 
12.4%
2 636
 
11.2%
1 488
 
8.6%
-1 99
 
1.7%
ValueCountFrequency (%)
-1 99
 
1.7%
0 802
14.1%
1 488
 
8.6%
2 636
 
11.2%
3 1061
18.7%
4 1886
33.2%
5 701
 
12.4%
ValueCountFrequency (%)
5 701
 
12.4%
4 1886
33.2%
3 1061
18.7%
2 636
 
11.2%
1 488
 
8.6%
0 802
14.1%
-1 99
 
1.7%

provincia
Categorical

High correlation 

Distinct28
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size567.1 KiB
Ciudad Autónoma de Buenos Aires
1795 
Buenos Aires
1435 
Córdoba
353 
Santa Fe
201 
Mendoza
 
169
Other values (23)
1720 

Length

Max length31
Median length19
Mean length16.381456
Min length1

Characters and Unicode

Total characters92932
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowChubut
2nd rowCiudad Autónoma de Buenos Aires
3rd rowCiudad Autónoma de Buenos Aires
4th rowCiudad Autónoma de Buenos Aires
5th rowCiudad Autónoma de Buenos Aires

Common Values

ValueCountFrequency (%)
Ciudad Autónoma de Buenos Aires 1795
31.6%
Buenos Aires 1435
25.3%
Córdoba 353
 
6.2%
Santa Fe 201
 
3.5%
Mendoza 169
 
3.0%
Chubut 150
 
2.6%
Misiones 127
 
2.2%
Rio Negro 125
 
2.2%
sin datos 123
 
2.2%
Entre Rios 103
 
1.8%
Other values (18) 1092
19.2%

Length

2025-07-08T11:19:56.603122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
buenos 3230
21.0%
aires 3230
21.0%
ciudad 1795
11.7%
de 1795
11.7%
autónoma 1795
11.7%
córdoba 353
 
2.3%
santa 281
 
1.8%
fe 201
 
1.3%
mendoza 169
 
1.1%
chubut 150
 
1.0%
Other values (29) 2395
15.6%

Most occurring characters

ValueCountFrequency (%)
9721
10.5%
e 9697
10.4%
u 7797
 
8.4%
s 7306
 
7.9%
o 6691
 
7.2%
n 6380
 
6.9%
d 6167
 
6.6%
a 6075
 
6.5%
i 5938
 
6.4%
A 5025
 
5.4%
Other values (31) 22135
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 92932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9721
10.5%
e 9697
10.4%
u 7797
 
8.4%
s 7306
 
7.9%
o 6691
 
7.2%
n 6380
 
6.9%
d 6167
 
6.6%
a 6075
 
6.5%
i 5938
 
6.4%
A 5025
 
5.4%
Other values (31) 22135
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 92932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9721
10.5%
e 9697
10.4%
u 7797
 
8.4%
s 7306
 
7.9%
o 6691
 
7.2%
n 6380
 
6.9%
d 6167
 
6.6%
a 6075
 
6.5%
i 5938
 
6.4%
A 5025
 
5.4%
Other values (31) 22135
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 92932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9721
10.5%
e 9697
10.4%
u 7797
 
8.4%
s 7306
 
7.9%
o 6691
 
7.2%
n 6380
 
6.9%
d 6167
 
6.6%
a 6075
 
6.5%
i 5938
 
6.4%
A 5025
 
5.4%
Other values (31) 22135
23.8%

cant_socios
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63528997
Minimum0
Maximum5
Zeros3773
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size88.6 KiB
2025-07-08T11:19:56.675466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0537741
Coefficient of variation (CV)1.6587293
Kurtosis2.245624
Mean0.63528997
Median Absolute Deviation (MAD)0
Skewness1.6880006
Sum3604
Variance1.1104399
MonotonicityNot monotonic
2025-07-08T11:19:56.745177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 3773
66.5%
1 784
 
13.8%
2 699
 
12.3%
3 275
 
4.8%
4 113
 
2.0%
5 29
 
0.5%
ValueCountFrequency (%)
0 3773
66.5%
1 784
 
13.8%
2 699
 
12.3%
3 275
 
4.8%
4 113
 
2.0%
5 29
 
0.5%
ValueCountFrequency (%)
5 29
 
0.5%
4 113
 
2.0%
3 275
 
4.8%
2 699
 
12.3%
1 784
 
13.8%
0 3773
66.5%

cant_apercibimientos
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size376.7 KiB
0.0
5673 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17019
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5673
100.0%

Length

2025-07-08T11:19:56.823797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:56.873952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5673
100.0%

Most occurring characters

ValueCountFrequency (%)
0 11346
66.7%
. 5673
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11346
66.7%
. 5673
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11346
66.7%
. 5673
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11346
66.7%
. 5673
33.3%

cant_suspensiones
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size376.7 KiB
0.0
5665 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17019
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5665
99.9%
1.0 8
 
0.1%

Length

2025-07-08T11:19:56.931253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:56.977067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5665
99.9%
1.0 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 11338
66.6%
. 5673
33.3%
1 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11338
66.6%
. 5673
33.3%
1 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11338
66.6%
. 5673
33.3%
1 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11338
66.6%
. 5673
33.3%
1 8
 
< 0.1%

cant_antecedentes
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size376.7 KiB
0.0
5661 
1.0
 
9
2.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17019
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5661
99.8%
1.0 9
 
0.2%
2.0 3
 
0.1%

Length

2025-07-08T11:19:57.036744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:57.088571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5661
99.8%
1.0 9
 
0.2%
2.0 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 11334
66.6%
. 5673
33.3%
1 9
 
0.1%
2 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11334
66.6%
. 5673
33.3%
1 9
 
0.1%
2 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11334
66.6%
. 5673
33.3%
1 9
 
0.1%
2 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11334
66.6%
. 5673
33.3%
1 9
 
0.1%
2 3
 
< 0.1%

cant_Apoderado
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size376.7 KiB
1.0
5002 
2.0
572 
0.0
 
99

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17019
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5002
88.2%
2.0 572
 
10.1%
0.0 99
 
1.7%

Length

2025-07-08T11:19:57.151473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:57.211830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5002
88.2%
2.0 572
 
10.1%
0.0 99
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 5772
33.9%
. 5673
33.3%
1 5002
29.4%
2 572
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5772
33.9%
. 5673
33.3%
1 5002
29.4%
2 572
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5772
33.9%
. 5673
33.3%
1 5002
29.4%
2 572
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5772
33.9%
. 5673
33.3%
1 5002
29.4%
2 572
 
3.4%

cant_representante
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size376.7 KiB
0.0
5673 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17019
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5673
100.0%

Length

2025-07-08T11:19:57.289017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:57.335313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5673
100.0%

Most occurring characters

ValueCountFrequency (%)
0 11346
66.7%
. 5673
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11346
66.7%
. 5673
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11346
66.7%
. 5673
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11346
66.7%
. 5673
33.3%

cant_autenticado
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size376.7 KiB
1.0
5198 
2.0
 
375
0.0
 
100

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17019
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5198
91.6%
2.0 375
 
6.6%
0.0 100
 
1.8%

Length

2025-07-08T11:19:57.393599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:57.446932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5198
91.6%
2.0 375
 
6.6%
0.0 100
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 5773
33.9%
. 5673
33.3%
1 5198
30.5%
2 375
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5773
33.9%
. 5673
33.3%
1 5198
30.5%
2 375
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5773
33.9%
. 5673
33.3%
1 5198
30.5%
2 375
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5773
33.9%
. 5673
33.3%
1 5198
30.5%
2 375
 
2.2%

cant_noAutenticado
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size376.7 KiB
0.0
5475 
1.0
 
198

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17019
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5475
96.5%
1.0 198
 
3.5%

Length

2025-07-08T11:19:57.518810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:57.566992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5475
96.5%
1.0 198
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 11148
65.5%
. 5673
33.3%
1 198
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11148
65.5%
. 5673
33.3%
1 198
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11148
65.5%
. 5673
33.3%
1 198
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11148
65.5%
. 5673
33.3%
1 198
 
1.2%

cant_sinMontoLimite
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size376.7 KiB
1.0
5002 
2.0
572 
0.0
 
99

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17019
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5002
88.2%
2.0 572
 
10.1%
0.0 99
 
1.7%

Length

2025-07-08T11:19:57.625936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:57.675888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5002
88.2%
2.0 572
 
10.1%
0.0 99
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 5772
33.9%
. 5673
33.3%
1 5002
29.4%
2 572
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5772
33.9%
. 5673
33.3%
1 5002
29.4%
2 572
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5772
33.9%
. 5673
33.3%
1 5002
29.4%
2 572
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5772
33.9%
. 5673
33.3%
1 5002
29.4%
2 572
 
3.4%

cant_MontoLimite
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size376.7 KiB
0.0
5673 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17019
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5673
100.0%

Length

2025-07-08T11:19:57.738405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:57.781549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5673
100.0%

Most occurring characters

ValueCountFrequency (%)
0 11346
66.7%
. 5673
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11346
66.7%
. 5673
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11346
66.7%
. 5673
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11346
66.7%
. 5673
33.3%

total_articulos_provee
Real number (ℝ)

Zeros 

Distinct543
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.884188
Minimum0
Maximum6993
Zeros99
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size88.6 KiB
2025-07-08T11:19:57.843713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median6
Q336
95-th percentile359
Maximum6993
Range6993
Interquartile range (IQR)35

Descriptive statistics

Standard deviation223.68421
Coefficient of variation (CV)3.3443511
Kurtosis304.36759
Mean66.884188
Median Absolute Deviation (MAD)5
Skewness13.297675
Sum379434
Variance50034.624
MonotonicityNot monotonic
2025-07-08T11:19:57.946287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1595
28.1%
2 404
 
7.1%
3 268
 
4.7%
4 207
 
3.6%
5 193
 
3.4%
6 172
 
3.0%
7 108
 
1.9%
8 107
 
1.9%
0 99
 
1.7%
9 91
 
1.6%
Other values (533) 2429
42.8%
ValueCountFrequency (%)
0 99
 
1.7%
1 1595
28.1%
2 404
 
7.1%
3 268
 
4.7%
4 207
 
3.6%
5 193
 
3.4%
6 172
 
3.0%
7 108
 
1.9%
8 107
 
1.9%
9 91
 
1.6%
ValueCountFrequency (%)
6993 1
< 0.1%
5612 1
< 0.1%
4867 1
< 0.1%
4471 1
< 0.1%
2868 1
< 0.1%
2189 1
< 0.1%
1935 1
< 0.1%
1742 1
< 0.1%
1715 1
< 0.1%
1681 1
< 0.1%
Distinct20
Distinct (%)0.4%
Missing26
Missing (%)0.5%
Memory size495.2 KiB
(377939.298, 599760.0]
 
346
(33011.111, 104767.373]
 
344
(1302657.558, 1793326.755]
 
337
(104767.373, 224078.198]
 
336
(224078.198, 377939.298]
 
332
Other values (15)
3952 

Length

Max length29
Median length28
Mean length24.500089
Min length19

Characters and Unicode

Total characters138352
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(9424898.401, 13557176.81]
2nd row(4727330.113, 6702697.888]
3rd row(46718747.516, 89439449.702]
4th row(46718747.516, 89439449.702]
5th row(222964579.98, 46172150151.0]

Common Values

ValueCountFrequency (%)
(377939.298, 599760.0] 346
 
6.1%
(33011.111, 104767.373] 344
 
6.1%
(1302657.558, 1793326.755] 337
 
5.9%
(104767.373, 224078.198] 336
 
5.9%
(224078.198, 377939.298] 332
 
5.9%
(890758.9, 1302657.558] 330
 
5.8%
(599760.0, 890758.9] 319
 
5.6%
(1793326.755, 2483085.385] 310
 
5.5%
(2483085.385, 3396600.0] 301
 
5.3%
(-0.001, 33011.111] 301
 
5.3%
Other values (10) 2391
42.1%

Length

2025-07-08T11:19:58.043690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
104767.373 680
 
6.0%
377939.298 678
 
6.0%
224078.198 668
 
5.9%
1302657.558 667
 
5.9%
599760.0 665
 
5.9%
890758.9 649
 
5.7%
1793326.755 647
 
5.7%
33011.111 645
 
5.7%
2483085.385 611
 
5.4%
3396600.0 601
 
5.3%
Other values (11) 4783
42.3%

Most occurring characters

ValueCountFrequency (%)
7 13378
9.7%
1 12696
9.2%
3 12026
 
8.7%
9 11929
 
8.6%
. 11294
 
8.2%
0 10827
 
7.8%
8 10781
 
7.8%
5 10221
 
7.4%
2 7706
 
5.6%
6 7332
 
5.3%
Other values (6) 30162
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 138352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 13378
9.7%
1 12696
9.2%
3 12026
 
8.7%
9 11929
 
8.6%
. 11294
 
8.2%
0 10827
 
7.8%
8 10781
 
7.8%
5 10221
 
7.4%
2 7706
 
5.6%
6 7332
 
5.3%
Other values (6) 30162
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 138352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 13378
9.7%
1 12696
9.2%
3 12026
 
8.7%
9 11929
 
8.6%
. 11294
 
8.2%
0 10827
 
7.8%
8 10781
 
7.8%
5 10221
 
7.4%
2 7706
 
5.6%
6 7332
 
5.3%
Other values (6) 30162
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 138352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 13378
9.7%
1 12696
9.2%
3 12026
 
8.7%
9 11929
 
8.6%
. 11294
 
8.2%
0 10827
 
7.8%
8 10781
 
7.8%
5 10221
 
7.4%
2 7706
 
5.6%
6 7332
 
5.3%
Other values (6) 30162
21.8%
Distinct10
Distinct (%)0.2%
Missing26
Missing (%)0.5%
Memory size423.6 KiB
(0.999, 2.0]
3343 
(2.0, 3.0]
522 
(3.0, 4.0]
 
333
(8.0, 12.0]
 
254
(19.0, 39.0]
 
227
Other values (5)
968 

Length

Max length14
Median length12
Mean length11.523995
Min length10

Characters and Unicode

Total characters65076
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(39.0, 1214.0]
2nd row(6.0, 8.0]
3rd row(39.0, 1214.0]
4th row(19.0, 39.0]
5th row(19.0, 39.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 3343
58.9%
(2.0, 3.0] 522
 
9.2%
(3.0, 4.0] 333
 
5.9%
(8.0, 12.0] 254
 
4.5%
(19.0, 39.0] 227
 
4.0%
(12.0, 19.0] 208
 
3.7%
(6.0, 8.0] 207
 
3.6%
(39.0, 1214.0] 199
 
3.5%
(4.0, 5.0] 193
 
3.4%
(5.0, 6.0] 161
 
2.8%
(Missing) 26
 
0.5%

Length

2025-07-08T11:19:58.147296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:58.242421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 3865
34.2%
0.999 3343
29.6%
3.0 855
 
7.6%
4.0 526
 
4.7%
12.0 462
 
4.1%
8.0 461
 
4.1%
19.0 435
 
3.9%
39.0 426
 
3.8%
6.0 368
 
3.3%
5.0 354
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 11294
17.4%
. 11294
17.4%
9 10890
16.7%
( 5647
8.7%
, 5647
8.7%
5647
8.7%
] 5647
8.7%
2 4526
7.0%
1 1295
 
2.0%
3 1281
 
2.0%
Other values (4) 1908
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11294
17.4%
. 11294
17.4%
9 10890
16.7%
( 5647
8.7%
, 5647
8.7%
5647
8.7%
] 5647
8.7%
2 4526
7.0%
1 1295
 
2.0%
3 1281
 
2.0%
Other values (4) 1908
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11294
17.4%
. 11294
17.4%
9 10890
16.7%
( 5647
8.7%
, 5647
8.7%
5647
8.7%
] 5647
8.7%
2 4526
7.0%
1 1295
 
2.0%
3 1281
 
2.0%
Other values (4) 1908
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11294
17.4%
. 11294
17.4%
9 10890
16.7%
( 5647
8.7%
, 5647
8.7%
5647
8.7%
] 5647
8.7%
2 4526
7.0%
1 1295
 
2.0%
3 1281
 
2.0%
Other values (4) 1908
 
2.9%

dtotal_articulos_provee
Categorical

High correlation  Missing 

Distinct15
Distinct (%)0.3%
Missing99
Missing (%)1.7%
Memory size424.7 KiB
(0.999, 2.0]
1999 
(4.0, 6.0]
365 
(345.0, 6993.0]
297 
(161.0, 345.0]
274 
(2.0, 3.0]
268 
Other values (10)
2371 

Length

Max length15
Median length12
Mean length11.88249
Min length10

Characters and Unicode

Total characters66233
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(58.0, 97.6]
2nd row(161.0, 345.0]
3rd row(58.0, 97.6]
4th row(58.0, 97.6]
5th row(40.0, 58.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 1999
35.2%
(4.0, 6.0] 365
 
6.4%
(345.0, 6993.0] 297
 
5.2%
(161.0, 345.0] 274
 
4.8%
(2.0, 3.0] 268
 
4.7%
(97.6, 161.0] 267
 
4.7%
(40.0, 58.0] 252
 
4.4%
(8.0, 11.0] 251
 
4.4%
(15.0, 21.0] 246
 
4.3%
(58.0, 97.6] 238
 
4.2%
Other values (5) 1117
19.7%

Length

2025-07-08T11:19:58.354224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.0 2267
20.3%
0.999 1999
17.9%
6.0 580
 
5.2%
4.0 572
 
5.1%
345.0 571
 
5.1%
161.0 541
 
4.9%
97.6 505
 
4.5%
58.0 490
 
4.4%
40.0 488
 
4.4%
11.0 482
 
4.3%
Other values (6) 2653
23.8%

Most occurring characters

ValueCountFrequency (%)
. 11148
16.8%
0 11131
16.8%
9 7560
11.4%
( 5574
8.4%
, 5574
8.4%
5574
8.4%
] 5574
8.4%
2 3205
 
4.8%
1 2997
 
4.5%
6 1923
 
2.9%
Other values (5) 5973
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 11148
16.8%
0 11131
16.8%
9 7560
11.4%
( 5574
8.4%
, 5574
8.4%
5574
8.4%
] 5574
8.4%
2 3205
 
4.8%
1 2997
 
4.5%
6 1923
 
2.9%
Other values (5) 5973
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 11148
16.8%
0 11131
16.8%
9 7560
11.4%
( 5574
8.4%
, 5574
8.4%
5574
8.4%
] 5574
8.4%
2 3205
 
4.8%
1 2997
 
4.5%
6 1923
 
2.9%
Other values (5) 5973
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 11148
16.8%
0 11131
16.8%
9 7560
11.4%
( 5574
8.4%
, 5574
8.4%
5574
8.4%
] 5574
8.4%
2 3205
 
4.8%
1 2997
 
4.5%
6 1923
 
2.9%
Other values (5) 5973
9.0%

cluster_k5
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size365.6 KiB
4
5673 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5673
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 5673
100.0%

Length

2025-07-08T11:19:58.431756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:58.474764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 5673
100.0%

Most occurring characters

ValueCountFrequency (%)
4 5673
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 5673
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 5673
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 5673
100.0%

Interactions

2025-07-08T11:19:51.995397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:49.321731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:49.851857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:50.316547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:50.790328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:51.354465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:52.095621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:49.413892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:49.936288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:50.401107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:50.875062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:51.521752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:52.187024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:49.502189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:50.008104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:50.477899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:50.959329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:51.627423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:52.280451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:49.587759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:50.085248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:50.549161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:51.036270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:51.735245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:52.387132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:49.680962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:50.162309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:50.631948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:51.139709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:51.823830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:52.482528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:49.763472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:50.234911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:50.707937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:51.224785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:51.900862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-08T11:19:58.534678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_antecedentescant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_sinMontoLimitecant_socioscant_suspensionesdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
Estado1.0000.1640.1120.1290.1710.2380.1860.0830.0000.1710.0790.3540.1050.1100.1250.0001.0000.1430.000
TipoSocietario0.1641.0000.1000.1190.2260.0000.1290.1310.0000.2260.4720.0000.0560.1400.0770.0831.0000.3530.000
anio_preinscripcion0.1120.1001.0001.0000.7090.0180.7040.0950.0470.7090.1080.0450.1230.0990.0660.0210.9990.3280.027
antiguedad0.1290.1191.0001.0000.7090.0260.7040.0970.3200.7090.2290.0490.1430.1110.0800.145-0.8690.3800.207
cant_Apoderado0.1710.2260.7090.7091.0000.0000.9000.5640.0001.0000.1820.0000.0790.1110.0680.0281.0000.6340.000
cant_antecedentes0.2380.0000.0180.0260.0001.0000.0000.0000.0000.0000.0000.9430.0620.0330.0510.0000.0000.0000.000
cant_autenticado0.1860.1290.7040.7040.9000.0001.0000.0510.0000.9000.1540.0000.0590.0920.0540.0370.9950.6290.000
cant_noAutenticado0.0830.1310.0950.0970.5640.0000.0511.0000.0300.5640.1300.0000.0800.0740.0220.0170.0170.0510.000
cant_procesos_adjudicado0.0000.0000.0470.3200.0000.0000.0000.0301.0000.0000.1360.0100.2790.1180.0940.566-0.2970.0000.352
cant_sinMontoLimite0.1710.2260.7090.7091.0000.0000.9000.5640.0001.0000.1820.0000.0790.1110.0680.0281.0000.6340.000
cant_socios0.0790.4720.1080.2290.1820.0000.1540.1300.1360.1821.0000.0000.0650.1380.0920.233-0.1960.1870.133
cant_suspensiones0.3540.0000.0450.0490.0000.9430.0000.0000.0100.0000.0001.0000.0770.0500.0660.0000.0000.0000.000
dcant_procesos_adjudicado0.1050.0560.1230.1430.0790.0620.0590.0800.2790.0790.0650.0771.0000.2110.1390.0820.0690.0590.097
dmonto_total_adjudicado0.1100.1400.0990.1110.1110.0330.0920.0740.1180.1110.1380.0500.2111.0000.0480.2230.0870.0710.034
dtotal_articulos_provee0.1250.0770.0660.0800.0680.0510.0540.0220.0940.0680.0920.0660.1390.0481.0000.0201.0000.0420.214
monto_total_adjudicado0.0000.0830.0210.1450.0280.0000.0370.0170.5660.0280.2330.0000.0820.2230.0201.000-0.1210.0000.158
periodo_preinscripcion1.0001.0000.999-0.8691.0000.0000.9950.017-0.2971.000-0.1960.0000.0690.0871.000-0.1211.0000.893-0.108
provincia0.1430.3530.3280.3800.6340.0000.6290.0510.0000.6340.1870.0000.0590.0710.0420.0000.8931.0000.000
total_articulos_provee0.0000.0000.0270.2070.0000.0000.0000.0000.3520.0000.1330.0000.0970.0340.2140.158-0.1080.0001.000

Missing values

2025-07-08T11:19:52.733609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-08T11:19:52.991396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-08T11:19:53.158571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k5
027236909900EMR VENTAS & SERVICIOS04/10/2016InscriptoPersona Física201610201668.01.069618e+075.0Chubut0.00.00.00.01.00.01.00.01.00.068.0(9424898.401, 13557176.81](39.0, 1214.0](58.0, 97.6]4
130569211685Electricidad Chiclana de R. Santoianni y O.S. Rodriguez18/08/2016Desactualizado Por Documentos VencidosSociedades De Hecho20160820167.04.975162e+065.0Ciudad Autónoma de Buenos Aires1.00.00.00.01.00.01.00.01.00.0330.0(4727330.113, 6702697.888](6.0, 8.0](161.0, 345.0]4
330708415487YLUM S.A.24/08/2016InscriptoSociedad Anónima2016082016111.05.959313e+075.0Ciudad Autónoma de Buenos Aires1.00.00.00.01.00.01.00.01.00.083.0(46718747.516, 89439449.702](39.0, 1214.0](58.0, 97.6]4
630708538317CAROLS SA09/09/2016InscriptoSociedad Anónima201609201633.07.614297e+075.0Ciudad Autónoma de Buenos Aires2.00.00.00.01.00.01.00.01.00.092.0(46718747.516, 89439449.702](19.0, 39.0](58.0, 97.6]4
730521417311CONFECCIONES JOSE CONTARTESE Y CIA S.R.L.12/09/2016InscriptoSociedad Responsabilidad Limitada201609201637.01.823406e+095.0Ciudad Autónoma de Buenos Aires2.00.00.00.01.00.01.00.01.00.050.0(222964579.98, 46172150151.0](19.0, 39.0](40.0, 58.0]4
820305924076Suministros EDA13/10/2016InscriptoPersona Física2016102016147.09.393091e+075.0Ciudad Autónoma de Buenos Aires0.00.00.00.02.00.01.01.02.00.0107.0(89439449.702, 222964579.98](39.0, 1214.0](97.6, 161.0]4
920082883240SEGUMAX de HORACIO MIGUEL ESPOSITO18/10/2016Desactualizado Por Documentos VencidosPersona Física20161020162.01.216063e+065.0Ciudad Autónoma de Buenos Aires0.00.00.00.01.00.01.00.01.00.0263.0(890758.9, 1302657.558](0.999, 2.0](161.0, 345.0]4
1130707882510SABADO URSI S.A.20/09/2016InscriptoSociedad Anónima201609201680.05.823704e+085.0Ciudad Autónoma de Buenos Aires4.00.00.00.01.00.01.00.01.00.064.0(222964579.98, 46172150151.0](39.0, 1214.0](58.0, 97.6]4
1230708516852COOPERATIVA DE TRABAJO ARCANGEL LIMITADA19/10/2016InscriptoCooperativas20161020166.02.781002e+075.0Buenos Aires4.00.00.00.01.00.01.00.01.00.02.0(19975532.58, 30451916.51](5.0, 6.0](0.999, 2.0]4
1320230506060sin datos15/08/2016InscriptoPersona Física201608201612.01.209260e+085.0Ciudad Autónoma de Buenos Aires0.00.00.00.01.00.01.00.01.00.05.0(89439449.702, 222964579.98](8.0, 12.0](4.0, 6.0]4
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k5
1006030561699867Melos Ediciones Musicales S.A.06/09/2017InscriptoSociedad Anónima20170920171.01.133333e+064.0Ciudad Autónoma de Buenos Aires3.00.00.00.01.00.01.00.01.00.04.0(890758.9, 1302657.558](0.999, 2.0](3.0, 4.0]4
1006220149549987TRANSPORTES ROBOL07/02/2017Desactualizado Por Mantencion FormularioPersona Física20170220171.05.261905e+064.0Corrientes0.00.00.00.02.00.02.00.02.00.04.0(4727330.113, 6702697.888](0.999, 2.0](3.0, 4.0]4
1006324925253304ALBERTO FREDY MARTIN15/02/2021InscriptoPersona Física20210220211.01.398857e+060.0Buenos Aires0.00.00.00.01.00.01.00.01.00.029.0(1302657.558, 1793326.755](0.999, 2.0](21.0, 29.0]4
1006520328156742M&M01/09/2022InscriptoPersona Física20220920221.00.000000e+000.0Corrientes0.00.00.00.01.00.01.00.01.00.01.0(-0.001, 33011.111](0.999, 2.0](0.999, 2.0]4
1006720171591563BIOTECNIKA16/06/2022InscriptoPersona Física20220620221.02.986090e+060.0Buenos Aires0.00.00.00.01.00.01.00.01.00.022.0(2483085.385, 3396600.0](0.999, 2.0](21.0, 29.0]4
1006820293290416ELIAS MARTIN SEGURA26/08/2022InscriptoPersona Física20220820221.01.873469e+050.0Ciudad Autónoma de Buenos Aires0.00.00.00.01.00.01.00.01.00.01.0(104767.373, 224078.198](0.999, 2.0](0.999, 2.0]4
1006920240423759FEDERICO MARTIN NUÑEZ26/08/2022InscriptoPersona Física20220820221.03.226531e+050.0Ciudad Autónoma de Buenos Aires0.00.00.00.01.00.01.00.01.00.01.0(224078.198, 377939.298](0.999, 2.0](0.999, 2.0]4
1007120287286687LAZARTE MARIO03/08/2022InscriptoPersona Física20220820221.05.792102e+060.0Buenos Aires0.00.00.00.01.00.01.00.01.00.01.0(4727330.113, 6702697.888](0.999, 2.0](0.999, 2.0]4
1007430716441098LISTOS PARA RODAR SAS16/08/2019InscriptoOtras Formas Societarias20190820191.01.196939e+052.0Ciudad Autónoma de Buenos Aires2.00.00.00.01.00.01.00.01.00.019.0(104767.373, 224078.198](0.999, 2.0](15.0, 21.0]4
1007527331126530sin datos18/08/2022InscriptoPersona Física20220820221.09.367347e+040.0Ciudad Autónoma de Buenos Aires0.00.00.00.01.00.01.00.01.00.04.0(33011.111, 104767.373](0.999, 2.0](3.0, 4.0]4